Automated Line Labelling: Dataset for Contour Detection and 3D Reconstruction

Hari Santhanam, Nehal Doiphode, Jianbo Shi

Abstract: Understanding the finer details of a 3D object, its contours, is the first step toward a physical understanding of an object. Many real-world application domains require adaptable 3D object shape recognition models, usually with little training data. For this purpose, we develop the first automatically generated contour labeled dataset, bypassing manual human labeling. Using this dataset, we study the performance of current state-of-the-art instance segmentation algorithms on detecting and labeling the contours. We produce promising visual results with accurate contour prediction and labeling. We demonstrate that our finely labeled contours can help downstream tasks in computer vision, such as 3D reconstruction from a 2D image.